import psycopg2
from sklearn.externals import joblib


# map matching
# database
conn = psycopg2.connect(host="localhost", database="huining", user="postgres", password="123456")  # 连接数据库
datatable = "shroads4"
SRID = 4326

# dataset
svc_img = joblib.load("./map_matching/all_svc_train_model_proba.m")  # for SIAMM
svc_ohmm = joblib.load("./map_matching/ohmm_svm_3.m")  # for OHMM
img_roots = ["D:/BaiduNetdiskDownload/20190429/pic/original/", ""]
traj_roots = ["./map_matching/text/20190429/",  # 行车记录仪
              "./map_matching/text/20161003/"]  # OSM 注意这组数据use_img只能取False，因为没图片
trajectory_data_list = ["20190429_trajectory.txt", "20161003_trajectory.txt"]
ground_truth_list = ["20190429_groundtruth_list.txt", "20161003_groundtruth_list.txt"]
sigma_list = [7.80e-5, 5.27e-5]  # degree
sigma_g_list = [4.549, 3.076]  # meter


# yolo
yolo_root = "H:/lvmiao/darknet-master/"
classes_path = yolo_root + "data/images/car.names"
cfg_path = yolo_root + "cfg/yolov3.cfg"  # r"cfg/yolov3-tiny-car.cfg" tiny是用我们的数据训练的，但是效果不如原始的
weights_path = yolo_root + "cfg/yolov3.weights"  # r"cfg/yolov3-tiny-car_10000.weights"

# lane
lane_param_path = "LaneParameters.txt"
if __name__ == "__main__":
    pass
